Modeling Smoke Transport and Dispersion

Diverse smoke management models are currently in use by various private and governmental agencies. These models were developed from basic algorithms developed in the mid-1960s to describe plume rise for industrial ducted emissions (see Background of plume models). Current models include such data as temperature of the fire, land area, flaming regions, fire plume duration, and emissions load (see Harms and Lavdas 1997, Jenkins et al. 2001, Latham 1993, Linn et al. 2002, Mercer and Weber 2001). Some recent models incorporate weather parameters such as wind speed, vertical lapse rates, barometric pressure, and moisture levels at varying altitudes to determine the extent to which atmospheric variation will affect smoke dispersion.

Plume Models

One of the simplest ways of estimating smoke concentrations is to assume that plumes diffuse in a Gaussian pattern along the centerline of a steady wind trajectory. Plume models usually assume steady-state conditions during the life of the plume, which means relatively constant emission rates, wind speed, and wind direction. For this reason, they can be used only to estimate concentrations relatively near the source or for a short duration. Their steady-state approximation also restricts plume models to conditions that do not include the influence of topography or significant changes in land use, such as flow from a forest to grassland or across a land-water boundary.

Plume models typically are in Lagrangian coordinates that follow particles or parcels as they move, assigning the positions in space of a particle or parcel at some arbitrarily selected moment. Examples adapted for wildland biomass smoke include VSMOKE (Harms and Lavdas 1997; Lavdas 1996) and SASEM (Riebau et al. 1988; Sestak and Riebau 1988). VSMOKE assumes that wind and other weather conditions are steady and constant over the area of consideration and during the time smoke will move from source to receptor. To predict smoke movement and dispersion, VSMOKE assumes that mean wind flow has dispersion capabilities due to wind movement caused by small random right angle deviations from the mean flow. As an example, the mean wind may be blowing from the west, with deviations from the south and north (causing horizontal dispersion). Additionally, right angle movement of the wind may be from below or above causing vertical dispersion.

Puff Models

Instead of describing smoke concentrations as a steadily growing plume, puff models characterize the source as individual puffs being released over time. Each puff expands in space in response to the turbulent atmosphere, which usually is approximated as a Gaussian dispersion pattern. Puffs move through the atmosphere according to the trajectory of their center position. Because puffs grow and move independently of each other, tortuous plume patterns in response to changing winds, varying topography, or alternating source strengths can be simulated with some accuracy. Most puff models are computed in Lagrangian coordinates.

Particle Models

In a particle model, the source is simulated by the release of many particles over the duration of the burn. The trajectory of each particle is determined as well as a random component that mimics the effect of atmospheric turbulence. This allows a cluster of particles to expand in space according to the patterns of atmospheric turbulence rather than following a parameterized spatial distribution pattern, such as common Gaussian approximations. Therefore, particle models tend to be the most accurate way of simulating concentrations at any point in time. Particle models use Lagrangian coordinates for accurate depiction of place for each period of particle movement (for example, Hysplit: Draxler and Hess 1998; PB-Piedmont: Achtemeier 1994, 2000).

Grid Models

Grid models use Eulerian coordinates, disperse pollutants uniformly within a cell, and transport them to adjacent cells. The simplicity of advection and diffusion in a grid model allows these models to more accurately simulate other characteristics of the plume, such as complex chemical or thermal interactions, and to be used over large domains with multiple sources. This is why grid models commonly are used for estimating regional haze and ozone and are often called Eulerian photochemical models. Much of the future work on fire impact assessment and planning at regional to national scales will be done using grid models.

Because of their nature, grid models are not used to define accurate timing or locations of smoke concentrations from individual plumes, only concentrations that fill each cell. This means that sources small relative to the grid size, which create individual plumes, will introduce unrealistic concentrations in places that are outside of the actual plume.